Although computing power has substantially increased during the last few years,
the horizontal resolution of present coupled GCMs is still too coarse to capture
the effects of local and regional forcings in areas of complex surface physiography
and to provide information suitable for many impact assessment studies. Since
IPCC (1992), significant progress has been achieved in the development and testing
of statistical downscaling and regional modeling techniques for the generation
of high-resolution regional climate information from coarse-resolution GCM simulations.

B.4.1. Statistical Downscaling

Statistical downscaling is a two-step process basically consisting of i) development
of statistical relationships between local climate variables (e.g., surface
air temperature and precipitation) and large-scale predictors, and ii) application
of such relationships to the output of GCM experiments to simulate local climate
characteristics. A range of statistical downscaling models have been developed
(IPCC 1996, WG I), mostly for U.S., European, and Japanese locations where better
data for model calibration are available. The main progress achieved in the
last few years has been the extension of many downscaling models from monthly
and seasonal to daily time scales, which allows the production of data more
suitable for a broader set of impact assessment models (e.g., agriculture or
hydrologic models).

When optimally calibrated, statistical downscaling models have been quite successful
in reproducing different statistics of local surface climatology (IPCC 1996,
WG I). Limited applications of statistical downscaling models to the generation
of climate change scenarios has occurred showing that in complex physiographic
settings local temperature and precipitation change scenarios generated using
downscaling methods were significantly different from, and had a finer spatial
scale structure than, those directly interpolated from the driving GCMs (IPCC
1996, WG I).

B.4.2. Regional Modeling

The (one-way) nested modeling technique has been increasingly applied to climate
change studies in the last few years. This technique consists of using output
from GCM simulations to provide initial and driving lateral meteorological boundary
conditions for high-resolution Regional Climate Model (RegCM) simulations, with
no feedback from the RegCM to the driving GCM. Hence, a regional increase in
resolution can be attained through the use of nested RegCMs to account for sub-GCM
grid-scale forcings. The most relevant advance in nested regional climate modeling
activities was the production of continuous RegCM multi-year climate simulations.
Previous regional climate change scenarios were mostly produced using samples
of month-long simulations (IPCC 1996, WG I). The primary improvement represented
by continuous long-term simulations consists of equilibration of model climate
with surface hydrology and simulation of the full seasonal cycle for use in
impact models. In addition, the capability of producing long-term runs facilitates
the coupling of RegCMs to other regional process models, such as lake models,
dynamical sea ice models, and possibly regional ocean (or coastal) and ecosystem
models.

Continuous month- or season-long to multi-year experiments for present-day
conditions with RegCMs driven either by analyses of observations or by GCMs
were generated for regions in North America, Asia, Europe, Australia, and Africa.
Equilibrium regional climate change scenarios due to doubled CO2 concentration
were produced for the continental U.S., Tasmania, Eastern Asia, and Europe.
None of these experiments included the effects of atmospheric aerosols.

In the experiments mentioned above, the model horizontal grid point spacing
varied in the range of 15 to 125 km and the length of runs from 1 month to 10
years. The main results of the validation and present-day climate experiments
with RegCMs can be summarized in the following points:

When driven by analyses of observations, RegCMs simulated realistic structure
and evolution of synoptic events. Averaged over regions on the order of 104-106
km2 in size, temperature biases were mostly in the range of a few
tenths of °C to a few °C, and precipitation biases were mostly in the range
of 10-40% of observed values. The biases generally increased as the size of
the region decreased.

The RegCM performance was critically affected by the quality of the driving
large-scale fields, and tended to deteriorate when the models were driven
by GCM output, mostly because of the poorer quality of the driving large-scale
data compared to the analysis data (e.g., position and intensity of storm
tracks).

Compared to the driving GCMs, RegCMs generally produced more realistic regional
detail of surface climate as forced by topography, large lake systems, or
narrow land masses. However, the validation experiments also showed that RegCMs
can both improve and degrade aspects of regional climate compared to the driving
GCM runs, especially when regionally averaged.

Overall, the models performed better at mid-latitudes than in tropical regions.

The RegCM performance improved as the resolution of the driving GCM increased,
mostly because the GCM simulation of large-scale circulation patterns improved
with increasing resolution.

Seasonal as well as diurnal temperature ranges were simulated reasonably
well.

An important problem in the validation of RegCMs has been the lack of adequately
dense observational data, since RegCMs can capture fine structure of climate
patterns. This problem is especially relevant in mountainous areas, where
only a relatively small number of high-elevation stations are often available.

When applied to the production of climate change scenarios, nested model experiments
showed the following (IPCC 1996, WG I):

For temperature, the differences between RegCM- and GCM-simulated region-averaged
change scenarios were in the range of 0.1 to 1.4°C. For precipitation, the
differences between RegCM and GCM scenarios were more pronounced than for
temperature, in some instances by one order of magnitude or even in sign.
These differences between RegCM- and GCM-produced regional scenarios are due
to the combined contributions of the different resolution of surface forcing
(e.g., topography, lakes, coastlines) and atmospheric circulations, and in
some instances the different behavior of model parameterizations designed
for the fine- and coarse-resolution models. In summer, differences between
RegCM and GCM results were generally more marked than in winter due to the
greater importance of local processes.

While the simulated temperature changes obtained with nested models were
generally larger than the corresponding biases, the precipitation changes
were generally of the same order of, or smaller than, the precipitation biases.

Finally, of relevance for the simulation of regional climate change is the
development of a variable-resolution global model technique, whereby the model
resolution gradually increases over the region of interest.